This is the notebook for the Month of May.

–

1. Loading the data from KK

source("LoadAndProcess.R")
head(kk)
names(kk)

head(kk.long)
names(kk.long)

–

2. Z-score densities within each (CHANNEL, SUBROI) aross all animals

–

3. Clustered heatmap of correlations

– # 4. Network plots

Dendrogram


library(ggdendro)
library(dplyr)
library(reshape2)

# cast by roi
sc1 = dcast(kl2.scale, CHANNEL+GROUP+EXPT+ID~ SUBROI, value.var = "z", 
            fun.aggregate=mean)

# make matrix
sc1.matrix = as.matrix(sc1[, -c(1:4)])

# make ID the row names
rownames(sc1.matrix) = sc1$ID

# dendro
dendro.sc1 = as.dendrogram(hclust(d = dist(x=sc1.matrix)))
dendro.sc1.p = ggdendrogram(data = dendro.sc1, rotate=T)

# print
print(dendro.sc1.p)


row.order = hclust(dist(sc1.matrix))$order
col.order = hclust(dist(t(sc1.matrix)))$order
sc1.new = sc1[row.order, col.order]

sc1.molten = melt(as.matrix(sc1.new))
ggplot(data = sc1.molten,
       aes(x = Var1, y = Var2, fill = value)) + 
    geom_raster() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1),
          axis.text.y = element_blank()) + 
    ggtitle("Clustered ggplot heatmap")

Heatmap from Dendro

library(RColorBrewer)
sc1.long = melt(sc1, id=c(1:4))
Error in melt(sc1, id = c(1:4)) : object 'sc1' not found
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